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Showing 18 results for Uncertainty

M.h. Baziar, R. Ziaie_moayed,
Volume 4, Issue 2 (6-2006)
Abstract

This paper highlights the effect of silt content on cone tip resistance in loose silty sand. In this study, twenty-seven cone penetration tests are performed in saturated silty sand samples with several different silt contents ranging from 10 to 50 percent. The samples are consolidated at three overburden stresses including 100, 200 and 300 kPa. It is shown that, as the silt content increases, the cone tip resistance decreases. In high percent of silt (30-50%), the cone tip resistance decreases more gently compared with low percent of silt (0-30%). It is also concluded that the method proposed by Olsen (1997) for stress normalization of cone tip resistance compared with the Robertson and Wride (1998) method has better agreement with the obtained results. To evaluate liquefaction potential of loose silty sand, the method presented by Robertson and Wride (1998) is also studied. The results showed that the use of Robertson and Wride (1998) method to estimate the fine content from CPT data causes some uncertainty especially for high silt content (FC>30%).
F. Amini, R. Vahdani,
Volume 5, Issue 3 (9-2007)
Abstract

In this research, an innovative numerical simulating approach for time domain analysis of multi degrees of freedom structures with uncertainty in dynamic properties is presented. A full scale finite element model of multi-story and multi bays of three sample structures has been constructed. The reduced order model of structure with holding the dominant and effective Gramians in the balanced state-space realization has been achieved for easy and safe design of the optimal control forces applied to the structure. Some easy selective control algorithms based on the Optimal-Stochastic control theories such as LQG, DLQRY and modified sliding mode control has been programmed with the simulation control sequences. Some real features of accurate control system such as time delay and noise signals in earthquake time histories and also measurement sensors are considered in illustrative simulation models. These models can be analyzed under either various intensity of corresponding earthquakes or desired random excitations passed through the suitable filters providing stochastic parameters of earthquake disturbances. This control procedure will be shown to be very efficient suppressing all the severities and difficulties may arise in design of a multi-objective optimal control system. The obtained results illustrate the feasibility and applicability of the proposed stochastic optimal control design of active control force providing a stable and energy-saving control strategy for tall building structures.
S.n. Moghaddas Tafreshi, A. Asakereh,
Volume 5, Issue 4 (12-2007)
Abstract

Conventional investigations on the behavior of reinforced and unreinforced soils are often investigated at the failure point. In this paper, a new concept of comparison of the behavior of reinforced and unreinforced soil by estimating the strength and strength ratio (deviatoric stress of reinforced sample to unreinforced sample) at various strain levels is proposed. A comprehensive set of laboratory triaxial compression tests was carried out on wet (natural water content) non-plastic beach silty sand with and without geotextile. The layer configurations used are one, two, three and four horizontal reinforcing layers in a triaxial test sample. The influences of the number of geotextile layers and confining pressure at 3%, 6%, 9%, 12% and 15% of the imposed strain levels on sample were studied and described. The results show that the trend and magnitude of strength ratio is different for various strain level. It implies that using failure strength from peak point or strength corresponding to the axial-strain approximately 15% to evaluate the enhancement of strength or strength ratio due to reinforcement may cause hazard and uncertainty in practical design. Hence, it is necessary to consider the strength of reinforced sample compared with unreinforced sample at the imposed strain level. Only one type of soil and one type of geotextile were used in all tests.
Kourosh Behzadian, Abdollah Ardeshir, Zoran Kapelan, Dragan Savic,
Volume 6, Issue 1 (3-2008)
Abstract

A novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.
M.h. Sebt, A. Gerei, H. Naghash Toosi,
Volume 7, Issue 3 (9-2009)
Abstract

Risks mean cases of uncertainty of project, the impact of which is realized as a threat (negative aspect) and/or opportunity (positive aspect). The traditional viewpoint on risk is a negative viewpoint that implies damages, loss and harmful consequences. Judgments such as this on risk merely emphasize on risks management and pay less attention to opportunities management. It is clear that some uncertainties might be profitable for the project as in many cases, it could be the source of loss. In a developed attitude, focus is made on a common process that could address the integrated management of both opportunities and risks to aim at maximizing the positive effectsopportunities-, and minimizing negative effects- risks-. Therefore, existence of causal-effect relations between risks, relationship, effects of risks and opportunities on each other and variety of strategies in facing risks gives no alternative for risk management team than taking integrated management of risks and opportunities. In another word, reaction to risks, with respect to risks and/or relevant opportunities, separately, will be never effective. In this paper, for the purpose of integrated management of risks and opportunities, the stages of quality analysis and reactions to risk are combined. The method which is used for reaction towards risk is a procedure based on dynamic system. Dynamic system is highly important among uncertainties due to considering the type and intensity of effects. By using dynamic system and attention to the relationship between uncertainties (risks/ opportunities), reaction to risk and decision making on employing suitable strategies to face risks will be more precise and accurate.
M. T. Banki, B. Esmaeili,
Volume 7, Issue 4 (12-2009)
Abstract

Cash flow forecasting is an indispensable tool for construction companies, and is essential for the survival

of any contractor at all stages of the work. The time available for a detailed pre-tender cash flow forecast is often

limited. Therefore, contractors require simpler and quicker techniques which would enable them to forecast cash flow

with reasonable accuracy. Forecasting S-curves in construction in developing countries like Iran in compare with

developed countries has many difficulties. It is because of uncertainty and unknown situation in nature of construction

industry of these countries. Based on knowledge of authors there is a little attempt for cash flow forecasting in

construction industry of Iran. As a result authors produced An S-curve equation for construction project from historical

data which has reasonable accuracy. A sample of 20 completed projects was collected and classified in to the three

different groups. In order to model S-curves for each group, a simple and reliable method of S curve fitting has been

used. S-curves were fitted into each group by using different techniques. Errors incurred when fitting these curves were

measured and compared with those associates in fitting individual projects. At the end, accuracy of each model has

been calculated and an equation has been proposed to forecast S-curves.


Jiuping Xu, Pei Wei,
Volume 10, Issue 1 (3-2012)
Abstract

In this paper, a location allocation (LA) problem in construction and demolition (C&D) waste management (WM) is studied. A bi-level model for this problem under a fuzzy random environment is presented where the upper level is the governments who sets up the processing centers, and the lower level are the administrators of different construction projects who control C&D waste and the after treatment materials supply. This model using an improved particle swarm optimization program based on a fuzzy random simulation (IPSO-based FRS) is able to handle practical issues. A case study is presented to illustrate the effectiveness of the proposed approach. Conclusions and future research directions are discussed.


A. Tarighat,
Volume 10, Issue 4 (12-2012)
Abstract

Chloride ion ingress in concrete is the main reason of concrete corrosion. In real world both uncertainty and stochasticity are

main attributes of almost all measurements including testing and modeling of chloride content profile in concrete. Regarding

these facts new models should be able to represent at least some of the uncertainties in the predictions. In this paper after

inspiration from classical physics related to diffusion and random walk concepts a stochastic partial differential equation (SPDE)

of diffusion is introduced to show a more realistic modeling/calibration scheme for construction of stochastic chloride content

profile in concrete. Diffusion SPDE provides a consistent quantitative way of relating uncertainty in inputs to uncertainty in

outputs. Although it is possible to run sensitivity analysis to get some statistical results from deterministic models but the nature

of diffusion is inherently stochastic. Brownian motion process (Wiener process) is used in SPDE to simulate the random nature

of the diffusion in heterogeneous media or random fields like concrete. The proposed method can be used to calibrate/model the

chloride ion profile in concrete by only some limited data for a given depth. Then the stochastic chloride ion diffusion can be

simulated by langevin equation. Results of the method are compared with data from some references and all show good

agreements.


A. Shariat Mohaymany, M. Babaei,
Volume 11, Issue 1 (3-2013)
Abstract

Since the 1990’s, network reliability has been considered as a new index for evaluating transportation networks under uncertainty. A large number of studies have been revealed in the literature in this field, which are mostly dedicated to developing relevant measures that can be utilized for the evaluation of vulnerable networks under different sources of uncertainty, such as daily traffic flow fluctuations, natural disasters, weather conditions, and so fourth. This paper addresses the resource allocation problem in vulnerable transportation networks, in which multiple performance reliability measures should be met at their desired levels, while the overall cost of upgrading links’ performances should be minimized simultaneously. For this purpose, a new approach has been considered to formulate the two well-known performance measures, connectivity and capacity reliability, along with their application in a bi-objective nonlinear mixed integer goal programming model. In order to take into account the uncertain conditions of supply, links’ capacities have been assumed to be random variables and follow normal distribution functions. A computationally efficient method has been developed that allows calculating the network-wise performance indices simply by means of a set of functions of links’ performance reliabilities. Using this approach, as the performance reliability of links are themselves functions of the random links’ capacities, they can be simply calculated through numerical integration. To achieve desirable levels for both connectivity reliability and capacity reliability (as network-wise performance reliability measures) two distinct objectives have been considered. One of the objectives seeks to maximize each of the measures regardless of what is happening to the other objective function which minimizes the budget. Since optimization models with two conflicting objectives cannot be solved directly, the well-known goal attainment multi-objective decision-making (MODM) approach has been adapted to formulate the model as a single objective model. Then the resultant single objective model has been solved through the generalized gradient method, which is a straightforward solution algorithm coded in existing commercial software such as MATLAB programming software. To show the applicability of the proposed model, numerical results are provided for a simple network. Also, to show the sensitiveness of the model to decision maker’s direction weights, the results of sensitivity analysis are presented..
K. J. Tu, Y. W. Huang,
Volume 11, Issue 4 (12-2013)
Abstract

The decisions made in the planning phase of a building project greatly affect its future operation and maintenance (O&M) cost. Recognizing the O&M cost of condominiums’ common facilities as a critical issue for home owners, this research aims to develop an artificial neural network (ANN) O&M cost prediction model to assist developers and architects in effectively assessing the impacts of their decisions made in the planning phase of condominium projects on future O&M costs. A regression cost prediction model was also developed as a benchmark model for testing the predictive accuracy of the ANN model. Six critical building design attributes (building age, number of apartment units, number of floors, average sale price, total floor area, and common facility floor area) which are usually available in the project planning phase, were identified as the input factors to both models and average monthly O&M cost as the output factor. 55 of the 65 existing condominium properties randomly selected were treated as the training samples whose data were used to develop the ANN and regression models the other ten as the test samples to compare and verify the predictive performance of both models. The study results revealed that the ANN model delivers more accurate and reliable cost prediction results, with lower average absolute error around 7.2% and maximum absolute error around 16.7%, as compared with the regression model. This study shows that ANN is an effective method in predicting building O&M costs in the project planning phase. Keywords: Project management, Facility management, Common facilities, Cost modeling.
S. Soudmand, M. Ghatee, S. M. Hashemi,
Volume 11, Issue 4 (12-2013)
Abstract

This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming problem. The objective function of the upper level problem is to minimize the difference between current LOS and desired LOS of links. In this level, to find optimal toll, a simulated annealing algorithm is used. The lower level problem is a fuzzy flow estimator model with fuzzy link costs. Applying a famous defuzzification function, a real-valued multi-commodity flow problem can be obtained. Then a polynomial time interior point algorithm is proposed to find the optimal solution regarding to the estimated flows. In pricing process, by imposing cost on some links with LOS F or E, users incline to use other links with better LOS and less cost. During the iteration of SA algorithm, the LOS of a lot of links gradually closes to their desired values and so the algorithm decreases the number of links with LOS worse than desirable LOS. Sioux Falls network is considered to illustrate the performance of SA-IP method on congestion pricing based on different LOS. In this pilot, after toll pricing, the number of links with LOS D, E and F are reduced and LOS of a great number of links becomes C. Also the value of objective function improves 65.97% after toll pricing process. It is shown optimal toll for considerable network is 5 dollar and by imposing higher toll, objective function will be worse.
A. Eslami, I. Tajvidi, M. Karimpour-Fard,
Volume 12, Issue 1 (1-2014)
Abstract

Three common approaches to determine the axial pile capacity based on static analysis and in-situ tests are presented, compared and evaluated. The Unified Pile Design (UPD), American Petroleum Institute (API) and a SPT based methods were chosen to be validated. The API is a common method to estimate the axial bearing capacity of piles in marine environments, where as the others are currently used by geotechnical engineers. Seventy pile load test records performed in the northern bank of Persian Gulf with SPT profile have been compiled for methods evaluation. In all cases, pile capacities were measured using full scale static compression and/or pull out loading tests. As the loading tests in some cases were in the format of proof test without reaching the plunging or ultimate bearing capacity, for interpretation the results, offset limit load criteria was employed. Three statistical and probability based approaches in the form of a systematic ranking, called Rank Index, RI, were utilized to evaluate the performance of predictive methods. Wasted Capacity Index (WCI) concept was also applied to validate the efficiency of current methods. The evaluations revealed that among these three predictive methods, the UPD is more accurate and cost effective than the others.
Yanfang Ma, Jiuping Xu,
Volume 12, Issue 2 (6-2014)
Abstract

In this paper, a bi-level decision making model is proposed for a vehicle routing problem with multiple decision-makers (VRPMD) in a fuzzy random environment. In our model, the objective of the leader is to minimize total costs by deciding the customer sets, while the follower is trying to minimize routing costs by choosing routes for each vehicle. Demand for each item has considerable uncertainty, so customer demand is considered a fuzzy random factor in this paper. After setting up the bi-level programming model for VRPMD, a bi-level global-local-neighbor particle swarm optimization with fuzzy random simulation (bglnPSO-frs) is developed to solve the bi-level fuzzy random model. Finally, the proposed model and method are applied to construction material transportation in the Yalong River Hydropower Base in China to illustrate its effectiveness.
A. Reyes-Salazar, E. Bojorquez, J.l. Rivera-Salas, A. Lopez-Barraza, H.e. Rodriguez-Lozoya,
Volume 13, Issue 3 (9-2015)
Abstract

The linear and nonlinear responses of steel buildings with perimeter moment resisting frames (PMRFs) are estimated and compared to those of equivalent buildings with spatial moment resisting frames (SMRFs). The equivalent models with SMRFs are designed by using an approximated procedure in such a way that, not only their fundamental period, total mass and lateral stiffness are fairly the same as those of the corresponding buildings with PMRFs, but also other characteristics to make the two structural "as equivalent" as possible. The numerical study indicates that the interstory shears of the PMRFs building may be significantly larger than those of the SMRFs building. The main reasons for this are that the buildings with PMRFs are stiffer and that the dynamics properties of the two types of structural systems are different. The interstory displacements are similar for both structural systems in many cases. For some other cases, however, they are larger for the model with SMRFs, depending upon the closeness between the earthquake corner periods and the periods of the buildings. The global ductility and story ductility demands are larger for the buildings with PMRFs, implying that, since larger ductility demands are imposed, the detailing of the connections will have to be more stringent than for the buildings with SMRFs. It can be concluded, that the seismic performance of the steel buildings with SMRFs may be superior to that of steel buildings with PMRFs. The findings of this paper are for the particular models used in the study. Much more research is needed to reach more general conclusions
Jiuping Xu, Qiurui Liu, Zhonghua Yang,
Volume 15, Issue 1 (1-2017)
Abstract

To fully explain hydropower unit operational problems, an optimal multi-objective dynamic scheduling model is presented which seeks to improve the efficiency of reservation regulation management. To reflect the actual hydropower engineering project environment, fuzzy random uncertainty and an integrated consideration of the natural resource constraints, such as load balance, system power balance, generation limits, turbine capacity, water head, discharge capacities, reservoir storage volumes, and water spillages, were included in the model. The aim of this research was to concurrently minimize discharges and maximize economic benefit. Subsequently, a new hybrid dynamic-programming based multi-start multi-objective simulated annealing algorithm was developed to solve the hydro unit operational problem. The proposed model and intelligent algorithm were then applied to the Xiaolongmen Hydraulic and Hydropower Station in China. The computational unit commitment schedule results demonstrated the practicality and efficiency of this optimization method.


Xiaoling Song, Jiuping Xu, Charles Shen, Feniosky Peña-Mora,
Volume 15, Issue 2 (3-2017)
Abstract

The construction temporary facilities layout planning (CTFLP) requires an identification of necessary construction temporary facilities (CTFs), an identification of candidate locations and a layout of CTFs at candidate locations. The CTFLP is particularly difficult and complex in large-scale construction projects as it affects the overall operation safety and effectiveness. This study proposes a decision making system to decide on an appropriate CTFLP in large-scale construction projects (e.g. dams and power plants) in a comprehensive way. The system is composed of the input, CTF identification, candidate location identification, layout optimization, evaluation and selection, as well as output stages. The fuzzy logic is employed to address the uncertain factors in real-world situations. In the input stage, the knowledge bases for identifying CTFs and candidate locations are determined. Then, CTFs and candidate locations are identified in the following two stages. In the mathematical optimization stage, a multiobjective mathematical optimization model with fuzzy parameters is established and fuzzy simulation-based Genetic Algorithm is proposed to obtain alternative CTFLPs. The intuitionistic fuzzy TOPSIS method is used to evaluate and select the most satisfactory CTFLP, which is output in the last stage. To demonstrate the effectiveness and efficacy of the proposed method, the CTFLP for the construction of a large-scale hydropower dam project is used as a practical application. The results show that the proposed system can assist the contractor to obtain an appropriate CTFLP in a more efficient and effective manner.


Laemthong Laokhongthavorn, Chalida U-Tapao,
Volume 15, Issue 2 (3-2017)
Abstract

This paper has applied operation research to solid waste disposal by which two objective functions are optimized to minimize the expected operational costs (maximize revenues) and the expected net carbon dioxide equivalent (CDE) emissions. Types and uncertain amounts of solid wastes as well as costs of electricity were factored into the selection decision of solid waste disposal, i.e. landfill, incineration, composting and recycling. An optimization model was applied to the solid waste disposal of Bangkok, Thailand. In addition, a multi-objective optimization technique was proposed for a tradeoff decision-making between minimum operational costs and CDE emissions. Composting and landfill are effective alternatives for Bangkok’s solid waste disposal system. The operational costs and net CDE emissions are highly correlated with the quantity of solid waste. Policy-makers and plant operators could adopt the proposed optimization model under uncertainty in the selection of an optimal solid waste disposal.


Mr Rakesh Bahera, Mr Anil Kumar, Dr. Lelitha Vanajakshi,
Volume 15, Issue 8 (12-2017)
Abstract

In recent times, Bus Arrival Time Prediction (BATP) systems are gaining more popularity in the field of Advanced Public transportation systems (APTS), a major functional area under Intelligent Transportation Systems (ITS). BATP systems aim to predict bus arrival times at various bus stops and provide the same to passenger’s pre-trip or while waiting at bus stops. A BATP system, which is accurate, is expected to attract more commuters to public transport, thus helping to reduce congestion. However, such accurate prediction of bus arrival still remains a challenge, especially under heterogeneous and lane-less traffic conditions such as the one existing in India. The uncertainty associated with such traffic is very high and hence the usual approach of prediction based on average speed will not be enough for accurate prediction. In order to make accurate predictions under such conditions, there is a need to identify correct inputs and suitable prediction methodology that can capture the variations in travel time. To accomplish the above goal, a robust framework relying on data analytics is proposed in this study. The spatial and temporal patterns in travel times were identified in real time by performing cluster analysis and the significant inputs thus identified were used for the prediction. The prediction algorithm used the Adaptive Kalman Filter approach, in order to take into account of the high variability in travel time. The proposed schemes were corroborated using real-world GPS data and the results obtained are very promising.



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